A Probabilistic Graph Model for Trust Opinion Estimation in Online Social Networks
Luke Liu, Qing Yang

TL;DR
This paper introduces a probabilistic graph model to improve trust opinion estimation in online social networks, enabling more accurate trust assessments for applications like lending and product reviews.
Contribution
It proposes a novel framework that converts trust parameters into a probabilistic graph model to better capture trustor biases and opinions.
Findings
Enhanced trust estimation accuracy demonstrated
Framework effectively models trustor biases
Applicable to various online trust-based applications
Abstract
Trust assessment plays a key role in many online applications, such as online money lending, product reviewing and active friending. Trust models usually employ a group of parameters to represent the trust relation between a trustor-trustee pair. These parameters are originated from the trustor's bias and opinion on the trustee. Naturally, these parameters can be regarded as a vector. To address this problem, we propose a framework to accurately convert the single values to the parameters needed by 3VSL. The framework firstly employs a probabilistic graph model (PGM) to derive the trustor's opinion and bias to his rating on the trustee.
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Taxonomy
TopicsAccess Control and Trust · Cloud Data Security Solutions · Privacy-Preserving Technologies in Data
